1. Field of the Invention
The present invention relates to an image-based super-resolution method using a cone-beam-based line-of-response (LOR) reconfiguration in a positron emission tomography (PET) image, and more particularly, to an apparatus and method for reconfiguring a super-resolution PET image using a cone-beam-based LOR reconfiguration. Further, the present invention relates to a technique for quickly and efficiently acquiring a point spread function (PSF) at each of all voxel locations.
2. Description of the Related Art
A positron emission tomography (PET) has been widely used as one of nuclear medicine test methods capable of three-dimensionally representing physiological, chemical and functional images of a human body using radiopharmaceuticals that emit positrons.
In general, the PET has been used to diagnose various types of cancers and may provide the useful results with respect to a differential diagnosis on a cancer, clinical staging, an evaluation of recurrence, and a determination as to the treatment effect. In addition, a receptor image or a metabolic image used for evaluating heart disease, brain disease, and brain functions may be acquired using the PET.
A positron emitted from a radioactive isotope consumes all of the self-kinetic energy during a very short period of time after its emission and is coupled with a neighboring electron, thereby becoming extinct. In this instance, two annihilation radiations, for example, gamma rays are emitted at an angle of 180 degrees.
A cylindrical PET scanner may detect the simultaneously emitted two annihilation radiations. When an image is reconfigured using the detected radiations, a location at which radiopharmaceuticals are gathered in a body and an amount of the gathered radiopharmaceuticals may be represented as a three-dimensional (3D) tomography image.
An expectation maximization (EM)-based iterative image reconfiguration method, such as a maximum likelihood expectation maximization (MLEM) algorithm, an ordered-subset expectation maximization (OSEM) algorithm, and a maximum a priori expectation maximization (MAP-EM) algorithm, for example, may be used as a PET image reconfiguration algorithm.
However, the PET image reconfiguration algorithm has a relatively low resolution and thus, a super-resolution algorithm of generating a plurality of sinogram sets having different sampling location through wobbling of a PET system and/or motion due to respiration of the subject. Accordingly, a plurality of reconfiguration processes is performed, leading to using a large amount of calculation and time. That is, a large amount of calculation and a large amount of time are used, which results in increasing an image reconfiguration complexity.
In general, a PET system may acquire data, for example, a line of response (LOR) from a PET detector as illustrated in block (a) of FIG. 1. FIG. 1, blocks (a), (b), and (c), illustrate an image reconfiguration preprocessing process according to a related art.
In the related art, to simplify an image reconfiguration algorithm, the PET system may perform a preprocessing process, such as single slice rebinning (SSRB) and arc correction, for example, on a sinogram as illustrated in blocks (b) and (c) of FIG. 1.
During the image reconfiguration preprocessing process according to the related art, a blur may occur in a sinogram. Thus, when an image reconfiguration is performed based on the sinogram, quality of a PET image may be degraded.
Also, in the related art, the image reconfiguration algorithm may be simplified through the preprocessing process such as the SSRB and the arc correction. On the contrary, accuracy may be decreased by a predetermined level and a parallel processing operation may not be performed on a forward projection and a back-projection. An LOR reconfiguration method according to the related art may be inappropriate for performing such parallel processing operation.
Moreover, it may be substantially impossible for a medical imaging system to measure a point spread function (PSF) with respect to all of voxel locations. Accordingly, an existing PSF acquiring technique may measure a PSF at a predetermined voxel location using a Monte-Carlo (MC) simulation.
However, when the medical imaging system measures a PSF with respect to all of voxel locations using only the MC simulation, a large amount of calculation may be required. Accordingly, a large amount of time may be used for processing and a storage memory may also use a large space.